function aName = train_initialization_parameters()

    close all;
    
    L = get_training_list();
    
    R = [];
    
    for iii = 1:size(L, 2)
        if (size(L{iii}, 2) > 0)
            L{iii}
            A = load(L{iii});
            R = [R; A];
        end
    end
    

    s1 = [];
    s2 = [];

    % Categorize results
    for iii = 1:size(R,1)
       if (R(iii,9) == 1)
          s1 = [s1; R(iii,3:7)]; 
       elseif (R(iii,9) == 0)
          s2 = [s2; R(iii,3:7)]; 
       end
    end
    
    % Now here, for each parameter, you will need to train a 1-D SVM...
    
    bestMeans = zeros(1,size(s1,2));
    
    for iii = 1:size(s1,2)
        
        bestMeans(iii) = 0;

        for jjj = 1:size(s1,1)
            bestMeans(iii) = bestMeans(iii) + s1(jjj,iii);
        end

        bestMeans(iii) = bestMeans(iii) / size(s1,1);
    
    end
    
    % Fix best mean for points in front to 1.00
    %bestMeans(1) = 0.00;
    bestMeans(3) = 1.00;
    
    
    hiVariances = zeros(1,size(s1,2));
    loVariances = zeros(1,size(s1,2));
    
    hiCount = 0;
    loCount = 0;
    
    for iii = 1:size(s1,2)
        
        hiVariances(iii) = 0;
        loVariances(iii) = 0;
        
        for jjj = 1:size(s1,1)
            if (s1(jjj,iii) > bestMeans(iii))
                hiCount = hiCount + 1;
                hiVariances(iii) = hiVariances(iii) + (abs(s1(jjj,iii) - bestMeans(iii)))^2;
            else
                loCount = loCount + 1;
                loVariances(iii) = loVariances(iii) + (abs(s1(jjj,iii) - bestMeans(iii)))^2;
            end
            
        end

        hiVariances(iii) = hiVariances(iii) / hiCount;
        hiVariances(iii) = sqrt(hiVariances(iii));
        
        loVariances(iii) = loVariances(iii) / loCount;
        loVariances(iii) = sqrt(loVariances(iii));
    
    end
    
    %hiVariances(4) = hiVariances(4)*10;
    %loVariances(4) = loVariances(4)*10;
    
    % Print these out to a file
    
    c = clock;
    timeString = sprintf('%04d-%02d-%02d-%02d-%02d-%02.0f', c(1), c(2), c(3), c(4), c(5), c(6));
    % outputFilename = sprintf('%s/../../config/scorecard_%s.txt', mfilename('fullpath'), timeString)
    Q = mfilename('fullpath');
    aName = sprintf('%s/config/scorecard_%s.txt', Q(1:end-41), timeString)
    
    results = [bestMeans; hiVariances; loVariances]
    
    fid = fopen(aName, 'w');
    %fid = fopen('abc.txt', 'w');
    
    fprintf(fid,'%1.4f %1.4f %1.4f %1.4f %2.3f\n', bestMeans(1), bestMeans(2), bestMeans(3), bestMeans(4), bestMeans(5));
    fprintf(fid,'%1.4f %1.4f %1.4f %1.4f %2.3f\n', hiVariances(1), hiVariances(2), hiVariances(3), hiVariances(4), hiVariances(5));
    fprintf(fid,'%1.4f %1.4f %1.4f %1.4f %2.3f\n', loVariances(1), loVariances(2), loVariances(3), loVariances(4), loVariances(5));
    
    fclose(fid);
    
    % Use these to generate 5 separate asymmetric gaussian functions
    
end